Improving Clustering of Gene Expression Patterns

The central question investigated in this project was whether clustering of gene expression patterns could be done more biologically accurate by providing the clustering technique with additional information about the genes as input besides the expression levels. With the term biologically accurate we mean that the genes should not only be clustered together according to their similarities in expression profiles, but also according to their functional similarity in terms of functional annotation and metabolic pathway. The data was collected at AstraZeneca R&D Mölndal Sweden and the applied computational technique was self-organizing maps. In our experiments we used the combination of expression profiles together with enzyme classification annotation as input for the self-organising maps instead of just the expression profiles. The results were evaluated both statistically and biologically. The statistical evaluation showed that our method resulted in a small decrease in terms of compactness and isolation. The biological evaluation showed that our method resulted in clusters with greater functional homogeneity with respect to enzyme classification, functional hierarchy and metabolic pathway annotation.